Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI

Deepa, S. N.; Janet, J; Sumathi, S.; Ananth, John Patrick

doi:10.1007/s10278-022-00752-2

Cited by 24 publications

(8 citation statements)

References 31 publications

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“…CNN is the feed forward network, in which the information flow is performed in one direction from input to output. 34 The input and the weights are convolved to generate a feature map as…”

Section: Cnn Featuresmentioning

confidence: 99%

“…CNN is the feed forward network, in which the information flow is performed in one direction from input to output 34 . The input and the weights are convolved to generate a feature map as

cf=g\left({w}^{\ast }S\right),

where g denotes activation function, w signifies weight, and S represents the segmented result.…”

Section: Proposed Competitive Swarm Multi‐verse Optimizer For Alzheim...mentioning

confidence: 99%

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Alzheimer's disease classification using competitive swarm multi‐verse optimizer‐based deep neuro‐fuzzy network

Allada

Bhavani

Chaduvula³

et al. 2023

Concurrency and Computation

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SummaryClassification of Alzheimer's disease (AD) from neuroimaging, like magnetic resonance imaging (MRI) through deep learning classifier has been increasing in research in recent decades. However, it is required for enhancing the accuracy of the AD classification for effective treatment. In this work, an efficient model termed competitive swarm multi‐verse optimizer + deep neuro‐fuzzy network (CSMVO + DNFN) is designed to accurately classify stages of AD. Preprocessing is done with a median filter. Then, the resulting image is segmented to find the interested regions by channel‐wise feature pyramid network module (CFPNet‐M). Some features obtained from the segmented image are haralick, convolutional neural network, and texture features. The devised method is more efficient in classifying different stages of AD with MRI modality. Furthermore, the developed model attained higher performance using metrics like the accuracy of 89.9%, sensitivity of 89.6%, and specificity of 87.0% based on the k‐fold value.

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“…CNN is the feed forward network, in which the information flow is performed in one direction from input to output. 34 The input and the weights are convolved to generate a feature map as…”

Section: Cnn Featuresmentioning

confidence: 99%

“…CNN is the feed forward network, in which the information flow is performed in one direction from input to output 34 . The input and the weights are convolved to generate a feature map as

cf=g\left({w}^{\ast }S\right),

where g denotes activation function, w signifies weight, and S represents the segmented result.…”

Section: Proposed Competitive Swarm Multi‐verse Optimizer For Alzheim...mentioning

confidence: 99%

Alzheimer's disease classification using competitive swarm multi‐verse optimizer‐based deep neuro‐fuzzy network

Allada

Bhavani

Chaduvula³

et al. 2023

Concurrency and Computation

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“…In addition, the findings of their study in tumor classification placed second in the testing round of the 2020 CPM-RadPath global competition. In [30] scientists use a powerful hybrid optimization system to categorize and divide brain tumors. In order to improve classification accuracy, features from a CNN are extracted.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Attention based MobileNetv2 Model for Brain Tumor Segmentation and Severity of Tumor Classification using Support Vector Machine

Pavithra,

Nidhya,

Vinothini

et al. 2023

Preprint

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Brain tumors are lumps of aberrant tissue that can develop into cancer and have a significant negative influence on a person's health. MRI scans of the brain can reveal them. Segmentation and classification are two elements in these approaches that are extremely crucial. As opposed to anatomical organ segmentation, tumor segmentation is much more difficult due to the variety in size, location, and shape of tumors. For this reason, it is imperative to build reliable, precise, and effective deep learning-based methods. Recent deep learning techniques for classifying and segmenting brain tumors produced encouraging results. These approaches, however, have heavy-weight architectures by nature, necessitating more storage and costly training procedures because of the enormous number of training parameters they must be fed. It is crucial to investigate transportable deep learning models without compromising classification precision. In this research, we provide compact deep neural network models using the pre-trained Attentiveness MobileNetV2 models along with the attention module. The four phases of the proposed system are preliminary processing, division, extracting and categorizing features, and severity classification. Anisotropic diffusion processing as well as data enhancement methods are used initially. The tumor region is then segmented using the proposed modified dimensional U-Net (3D-M-U-Net). Finally, the extraction and classification of features are implemented using the Compact MobileNetV2 framework. Here, the high-level tumor-based information is initially recovered from the convolution features. The important semantic information is then captured using an attention module. Once high-level tumor-based data as well as fascinating semantic information have been combined in the convolutional and focused modules, fully linked layers as well as the layer of softmax are utilized to categorize tumours into either benign or dangerous. Finally, Support Vector Machine (SVM) is used to categorize tumors into moderate, severe, and mild phases. The suggested approach was tested on the high-quality brain cancer images available in the Brats-2020 as well as Brats-2019 datasets. In regards to precision, recall, accuracy, F-Score, Dice Similarities Coefficient (DSC), as well as Structural Similarity Indicator Matrix (SSIM), the suggested model outperforms existing traditional and hybrid models. It was also the most effective and productive method tested. The suggested model has a 99.9% accuracy, a 99.9% precision, and a 99.8% recall across both datasets.

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“…An effective hybrid optimization approach was used in [ 114 ] for the segmentation and classification of brain tumors. To improve categorization, the CNN features were extracted.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

Kaifi

2023

Diagnostics

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Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

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Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI

Cited by 24 publications

References 31 publications

Alzheimer's disease classification using competitive swarm multi‐verse optimizer‐based deep neuro‐fuzzy network

Alzheimer's disease classification using competitive swarm multi‐verse optimizer‐based deep neuro‐fuzzy network

A Lightweight Attention based MobileNetv2 Model for Brain Tumor Segmentation and Severity of Tumor Classification using Support Vector Machine

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

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